1. Field of the Invention
The present invention concerns a method for controlling the image acquisition apparatus, as well as an image acquisition device, as well as an image acquisition device for implementing the method.
2. Description of the Prior Art
In order to be able to acquire qualitatively high-grade image data sets optimally suitable for a medical finding, not only does the acquisition region need to be determined with optimal precision but also other image acquisition parameters must also be adapted with regard to the patient or the type of examination. A large number of evaluation parameters are also required for a successful evaluation and preparation of the image data.
For this purpose, it is known to acquire planning image data sets, for example localizer image data sets in the case of magnetic resonance or low-dose planning image data sets in the case of computed tomography. The example of magnetic resonance will be discussed in detail, but similar problems also occur with other image acquisition modalities.
For diagnostic image acquisition with a magnetic resonance device, it is typical to initially acquire a localizer image data set. This is an image data set that can be acquired quickly due to special sequences, and typically such a localizer image data set shows large regions of a patient, or even the entire patient. The examination planning is conducted using these localizer image data sets. The localizers in particular serve to mark the volume of interest which, for example, contains an organ of interest, and to be able to correspondingly implement the slice planning. Additional parameters, in particular also patient-specific acquisition parameters, can be adapted by a trained operator from the localizer images.
The majority of these procedures today within the scope of the examination planning proceed manually. In the planning image data sets an operator must mark the volume of interest and, if necessary, additional regions of interest by hand, and must also set many acquisition parameters by hand. This results in the total time needed to acquire image data for the final (diagnostic) examination image data set that in all cases being lengthy, and non-standardized and cannot be reproduced.
A method for controlling the acquisition and/or evaluation operation of image data in medical examinations is known from DE 10 2006 017 932 A1. There it is proposed to use a statistical model of the target volume that is based on data about real anatomy, which model models a target volume (for example an organ) as a discrete polygonal mesh (thus geometrically). An average shape of the surface of the target volume is thus considered. This average shape should then be adapted to a shape determined from the planning image data set. This consequently deals with a position, orientation and shape of a special object, thus a geometric consideration.
DE 10 2007 019 514 A1 concerns a general framework for the image segmentation using ordered spatial dependency. This procedure is based on the recognition that structures can be more easily localized and identified when searches are conducted for their presence relative to other structures that are much easier to identify. Therefore the internal, structurally ordered spatial dependency should be used for a novel segmentation framework. The relative locations of the structures among one another are thus modeled.
Within the scope of minimally invasive procedures, segmentation and registration methods that use an atlas are known from the articles by S. Klein et al., “Segmentation of the prostrate in MR images by atlas matching”, in: 4th IEEE Int. Symp. On Biomedical Imaging: From Nano to Macro, 2007, 12-15 Apr. 2007, P. 1300-1303, as well as by F. J. S. Castro et al., “A Cross Validation Study of Deep Brain Stimulation Targeting: From Experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms” in: IEEE Trans. On Medical Imaging, Vol. 25, Iss. 11, November 2006, P. 1440-1450.